Consumer/Patient Experience Special Feature: Masters Of The Pharma Universe: 4 Databases Every Digital Marketer Must Know

Nearly two-thirds of U.S. senior business decision-makers rank big data/analytics first among the technologies necessary for enhancing the customer experience. A similar number put “improving data analysis capabilities” at the top of their priorities.

Essential Pharma Databases
Pharma databases provide everything from insight into your supply chain to detailed physician profiles. Often, they uncover regional, demographic, and other differences that may influence your digital marketing strategy. To make the most of these resources, it’s important to understand the strengths and limitations of each.

Drug Distribution DataDrug distribution data (DDD) tracks the distribution of branded products to retail and non-retail buyers in case units.
When Drug A comes off the manufacturing line, it’s delivered to hospitals and pharmacies all over the country. DDD lets you know the number of cases delivered to each location. For instance, you might learn that Hospital 1 took delivery of four cases, while Pharmacy X received one, and Pharmacy Y accepted two.
However, you won’t know where those drugs go from there. DDD data doesn’t identify the physicians who prescribe the drug or provide any information about their patients or diagnoses.

Prescription Claims DataPrescription claims data tracks all prescriptions that are dispensed through the mail, retail locations, and long-term care channels.
When Dr. Peterson prescribes Drug A and his patient fills the prescription at Pharmacy Y, the pharmacy files a claim with the insurance company. Aggregated claims data tracks the total number of prescriptions dispensed for a specific drug or therapeutic class by prescriber and geographic location. Data also can be analyzed by patient gender, age, and co-payment, as well as by method of payment.
Prescription claims data provides insight into geographic patterns of dispensing, but leaves many questions unanswered about the prescriber.

Electronic Health RecordsElectronic health records (EHR) and electronic medical records (EMR) indicate how a particular drug is used based on aggregated historical data.
An EHR contains data from all of the clinicians involved in a patient’s care, while an EMR includes a patient’s medical history from a single practice. Pharma marketers can use this aggregated data to understand how their brands are being used relative to specific diagnoses and patient groups over a given time period.

The AIM data set also reports the categories of medical websites physicians visit across our digital medical ecosystem and the types of professional content they read on those sites.

How Marketing Data Evolved
Once you have a handle on these four databases, it helps to understand how they evolved. Retail marketing provides a useful analogy. Take Budweiser as an example:

Historically, the company knew how many kegs and cases it sold to specific restaurants or bars.

Point-of-sale data then allowed Budweiser to determine if it sold more beer by the bottle or draft.

Eventually, retailers began to link transactions to customer accounts. The company could identify similarities and develop detailed buyer profiles.

At each stage, Budweiser refined its marketing and sales strategies. More precise customer profiles led to the creation of new products, messages, and markets. Over time, customers were better served and sales increased.

Yet all of these marketing decisions were based on aggregated data, so a little guesswork was still required. Pharma data followed a similar evolution, but audience identity management has recently taken it to the next level.

A Short History of Pharma Data
DDD was the gold standard in pharma for years. Without it, brands couldn’t measure the quantity of drugs sold. Even with it, you didn’t know which physicians were prescribing your drugs or for what conditions.

That began to change in the early 1990s. Pharma marketers could finally identify a physician and know which prescriptions he was writing. With aggregated claims data, marketers could begin to draw conclusions about common therapeutic uses of a particular brand, including its use by gender, age, and geographic region. Marketers applied this new insight to improve their targeting and build compensation and sales effectiveness models.

Over the next decade, digital record-keeping became widespread. Electronic medical records (EMR) and electronic health records (EHR) made it possible to analyze how various drugs were being administered, including their off-label uses and concomitant therapies. EHRs and EMRs enabled pharma marketers to pinpoint trends and engage physicians with branded messages at the moment they were making prescribing decisions.

Fast Forward to Now
In 2016, pharma data took its biggest leap yet. After years of relying on aggregated data – and therefore, guessing about physicians’ current needs and interests – pharma marketers gained access to real-time data about the online behavior of identified physicians.

Audience identity data lets you determine which pages an individual physician views on your brand site, how long he stays, and how often he visits. You can learn what website he came from and where he goes when he leaves.

If his digital journey includes visits to any of the hundreds of websites in our digital medical ecosystem, you can even use keyword data to gain knowledge about the professional content he reads on those sites.

Over time, audience identity data uncovers new insights into identified physicians that brands can use to optimize email marketing, website content, and sales rep interactions. Email campaigns and website initiatives can be integrated, and real-time content personalization becomes a real possibility.

More Precise Data Brings Greater Marketing Opportunities
Everywhere you look, pharma brands are exploring innovative ways to incorporate data into their marketing strategies. Although the idea of “big data” can be intimidating, it’s really just the evolution of digital marketing. Pharma marketers have been using data to refine campaigns for decades. Now that the data is more precise, the opportunities for developing increasingly effective campaigns are virtually unlimited.